Visualization

First, import and tidy data:

gender neighborhood VS hiv

neb_plot = hiv_data %>% 
  group_by(neighborhood, gender) %>% 
  filter(age != "All") %>%
  summarise(sum_hiv = sum(hiv_diagnoses)) %>% 
  ggplot(aes(x = reorder(neighborhood, sum_hiv), y = sum_hiv, color = gender)) + 
  coord_flip() +
  geom_point() +
  labs(
        title = "Gender and Neighborhood Influence on HIV Incidence",
        x = "Neighborhood",
        y = "HIV diagnoses",
        caption = "Data from the ..."
      )

ggplotly(neb_plot)

The number of HIV diagnoses is higher among male than female in all neighborhoods. Beford Stuyvesant - Crown Heights have the most HIV diagnoses for both men and women.

gender, age vs hiv

age_plot = hiv_data %>% 
  filter(age != "All") %>% 
  group_by(gender, age) %>% 
  summarise(sum_hiv = sum(hiv_diagnoses)) %>% 
  ggplot(aes(y = sum_hiv, x = age, fill = gender)) + 
  geom_bar(stat = "identity", alpha = 0.8, position = position_dodge()) +
  scale_fill_brewer(palette = "Dark2") +
  labs(
        title = "Gender and Age Influence on HIV Incidence",
        x = "Age range",
        y = "HIV diagnoses",
        caption = "Data from the ..."
      ) 

ggplotly(age_plot)

age_plot2 = hiv_data2 %>% 
  filter(borough == "All", neighborhood == "All", race == "All",  gender != "All", age != "All") %>% 
  group_by(gender, age) %>% 
  summarise(sum_hiv = sum(hiv_diagnoses)) %>% 
  ggplot(aes(y = sum_hiv, x = age, fill = gender)) + 
  geom_bar(stat = "identity", alpha = 0.8, position = position_dodge()) +
  scale_fill_brewer(palette = "Dark2") +
  labs(
        title = "Gender and Age Influence on HIV Incidence",
        x = "Age range",
        y = "HIV diagnoses",
        caption = "Data from the ..."
      ) 

ggplotly(age_plot2)

gender race vs hiv

race_plot = hiv_data %>% 
  filter(race != "All") %>% 
  group_by(gender, race) %>% 
  summarise(sum_hiv = sum(hiv_diagnoses)) %>% 
  ggplot(aes(y = sum_hiv, x = reorder(race, sum_hiv), fill = gender)) + 
  geom_bar(stat = "identity", alpha = 0.8, position = position_dodge()) +
  scale_fill_manual(values = c("#E69F00", "#56B4E9")) +
  labs(
        title = "Race and Gender Influence on HIV Incidence",
        x = "Race",
        y = "HIV diagnoses",
        caption = "Data from the ..."
      ) 

ggplotly(race_plot)

race_plot2 = hiv_data2 %>% 
  filter(borough == "All", neighborhood == "All", race != "All",  gender != "All", age == "All") %>% 
  group_by(gender, race) %>% 
  summarise(sum_hiv = sum(hiv_diagnoses)) %>% 
  ggplot(aes(y = sum_hiv, x = reorder(race, sum_hiv), fill = gender)) + 
  geom_bar(stat = "identity", alpha = 0.8, position = position_dodge()) +
  scale_fill_manual(values = c("#E69F00", "#56B4E9")) +
  labs(
        title = "Race and Gender Influence on HIV Incidence",
        x = "Race",
        y = "HIV diagnoses",
        caption = "Data from the ..."
      ) 

ggplotly(race_plot2)

hiv diagnoses in borough with most hiv over years

hiv_data2 %>%
  filter(borough != "All", neighborhood == "All", gender == "All", age == "All", race == "All") %>% 
  group_by(borough) %>% 
  summarize(sum_hiv = sum(hiv_diagnoses)) %>% 
  arrange(desc(sum_hiv))

## # A tibble: 5 x 2
##   borough       sum_hiv
##   <chr>           <int>
## 1 Brooklyn         3815
## 2 Manhattan        3536
## 3 Bronx            2736
## 4 Queens           2327
## 5 Staten Island     217

year_plot = hiv_data %>% 
  mutate(year = as.integer(year)) %>% 
  filter(borough == "Brooklyn" & gender == "Male" & age == "20 - 29") %>% 
  group_by(year, neighborhood) %>% 
  summarize(sum_hiv = sum(hiv_diagnoses)) %>% 
  ggplot(aes(x = year, y = sum_hiv, color = neighborhood)) +
  geom_line()
ggplotly(year_plot)

Limitations:

We can not visualize the effect of age and race at the same time.